One example of a machine-learning model is a recurrent neural-network, which can have one or more feedback loops to allow data to propagate in both forward and backward there-through. This can allow for information to persist within the recurrent neural-network. For example, a recurrent neural-network can determine an output based at least partially on information that the recurrent neural-network has seen before, giving the recurrent neural-network the ability to use previous input to inform the output.